Financial Risk Manager Part 1

Explanation

K-means clustering is the most suitable machine learning model for this task because:

• Clustering Purpose: K-means is specifically designed for grouping data points into distinct clusters based on similarity
• Fixed Number of Groups: The requirement to split customers into exactly 20 distinct groups aligns perfectly with K-means, where you specify the number of clusters (k=20)
• Customer Segmentation: This is a classic use case for clustering algorithms in customer analytics
• Scalability: K-means can efficiently handle 2,000 data points

Why other options are less suitable:

• Linear Regression: Used for predicting continuous outcomes, not grouping
• Logistic Regression: Used for binary classification, not multi-group clustering
• Decision Tree: Primarily for classification or regression, though can be used for clustering in some variations
• Support Vector Machine: Mainly for classification, though SVM clustering exists but is less common
• Neural Network: Can be used for clustering but is more complex and computationally intensive than K-means

K-means clustering is the standard and most appropriate choice for this customer segmentation task.